Consideration of Risks in Active Sensing for an Autonomous Vehicle

ABSTRACT

An autonomous vehicle configured for active sensing may also be configured to weigh expected information gains from active-sensing actions against risk costs associated with the active-sensing actions. An example method involves: (a) receiving information from one or more sensors of an autonomous vehicle, (b) determining a risk-cost framework that indicates risk costs across a range of degrees to which an active-sensing action can be performed, wherein the active-sensing action comprises an action that is performable by the autonomous vehicle to potentially improve the information upon which at least one of the control processes for the autonomous vehicle is based, (c) determining an information-improvement expectation framework across the range of degrees to which the active-sensing action can be performed, and (d) applying the risk-cost framework and the information-improvement expectation framework to determine a degree to which the active-sensing action should be performed.

CROSS-REFERENCE TO RELATED APPLICATIONS

This patent application is a continuation of U.S. application Ser. No.14/302,258, filed Jun. 11, 2014, which is a continuation-in-part of U.S.application Ser. No. 13/471,184, which was filed on May 14, 2012, bothof which are incorporated herein by reference in their entirety as iffully set forth in this application.

BACKGROUND

Unless otherwise indicated herein, the materials described in thissection are not prior art to the claims in this application and are notadmitted to be prior art by inclusion in this section.

Some vehicles are configured to operate in an autonomous mode in whichthe vehicle navigates through an environment with little or no inputfrom a driver. Such a vehicle typically includes one or more sensorsthat are configured to sense information about the environment. Thevehicle may use the sensed information to navigate through theenvironment. For example, if the sensors sense that the vehicle isapproaching an obstacle, the vehicle may navigate around the obstacle.

SUMMARY

In one aspect, a computer-implemented method involves: (i) receiving, bya computing system, information from one or more sensors of anautonomous vehicle, wherein one or more control processes for theautonomous vehicle are based upon the information; (ii) determining, bythe computing system, an information-improvement expectation thatcorresponds to an active-sensing action, wherein the active-sensingaction comprises an action that is performable by the autonomous vehicleto potentially improve the information upon which at least one of thecontrol processes for the autonomous vehicle is based; (iii)determining, by the computing system, a risk cost that corresponds tothe active-sensing action; (iv) determining, by the computing system,whether or not the risk cost is less than a threshold risk cost, whereinthe threshold risk cost corresponds to the information-improvementexpectation; (v) if the risk cost is less than the threshold risk cost,then initiating the active-sensing action; and (vi) if the risk cost isgreater than or equal than the threshold risk cost, then: (a) making anadjustment to the active-sensing action that affects the correspondinginformation-improvement expectation and the determined risk cost and (b)repeating (ii) to (iv) for the adjusted active-sensing action untileither the determined risk cost is less than the threshold risk cost, ora determination is made that no further adjustments should be made tothe active-sensing action.

In another aspect, an autonomous-vehicle system includes one or moresensor interfaces operable to receive data from one or more sensors ofan autonomous vehicle, and a computer system. The computer system isconfigured to: (i) receive, via the one or more sensor interfaces,information from the one or more sensors, wherein one or more controlprocesses for the autonomous vehicle are based upon the information;(ii) determine an information-improvement expectation that correspondsto an active-sensing action, wherein the active-sensing action comprisesan action that is performable by the autonomous vehicle to potentiallyimprove the information upon which at least one of the control processesfor the autonomous vehicle is based; (iii) determine a risk cost thatcorresponds to the active-sensing action; (iv) determine whether or notthe risk cost is less than a threshold risk cost, wherein the thresholdrisk cost corresponds to the information-improvement expectation; (v) ifthe risk cost is less than the threshold risk cost, then initiate theactive-sensing action; and (vi) if the risk cost is greater than orequal than the threshold risk cost, then: (a) make an adjustment to theactive-sensing action that affects the correspondinginformation-improvement expectation and the determined risk cost and (b)repeat (ii) to (iv) for the adjusted active-sensing action until eitherthe determined risk cost is less than the threshold risk cost, or adetermination is made that no further adjustments should be made to theactive-sensing action.

In yet another aspect, an example method involves: (a) receiving, by acomputing system, information from one or more sensors of an autonomousvehicle, wherein one or more control processes for the autonomousvehicle are based upon the information; (b) determining, by thecomputing system, a risk-cost framework that indicates risk costs acrossa range of degrees to which an active-sensing action can be performed,wherein the active-sensing action comprises an action that isperformable by the autonomous vehicle to potentially improve theinformation upon which at least one of the control processes for theautonomous vehicle is based; (c) determining, by the computing system,an information-improvement expectation framework that indicatesinformation-improvement expectations across the range of degrees towhich the active-sensing action can be performed; (d) applying, by thecomputing system, the risk-cost framework and theinformation-improvement expectation framework to determine a degree towhich the active-sensing action should be performed, wherein a score ofthe active-sensing action of the determined degree is less than athreshold score, wherein the score is determined based on the risk costand information-improvement expectation corresponding to the determineddegree; and (e) initiating the active-sensing action of the determineddegree.

In a further aspect, an autonomous-vehicle system includes one or moresensor interfaces operable to receive data from one or more sensors ofan autonomous vehicle, and a computer system. The computer system isconfigured to: (a) receive information from one or more sensors of anautonomous vehicle, wherein one or more control processes for theautonomous vehicle are based upon the information; (b) determine arisk-cost framework that indicates risk costs across a range of degreesto which an active-sensing action can be performed, wherein theactive-sensing action comprises an action that is performable by theautonomous vehicle to potentially improve the information upon which atleast one of the control processes for the autonomous vehicle is based;(c) determine an information-improvement expectation framework thatindicates information-improvement expectations across the range ofdegrees to which the active-sensing action can be performed; (d) applythe risk-cost framework and the information-improvement expectationframework to determine a degree to which the active-sensing actionshould be performed, wherein a score of the active-sensing action of thedetermined degree a score that is greater than a threshold score,wherein the score is determined based on the risk cost andinformation-improvement expectation corresponding to the determineddegree; and (e) initiate the active-sensing action of the determineddegree.

These as well as other aspects, advantages, and alternatives, willbecome apparent to those of ordinary skill in the art by reading thefollowing detailed description, with reference where appropriate to theaccompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a functional block diagram illustrating a vehicle, accordingto an example embodiment.

FIG. 2 shows a vehicle, according to an example embodiment.

FIG. 3 is a simplified flow chart illustrating a method, according to anexample embodiment.

FIG. 4 is a top-down view of an autonomous vehicle operating scenario,according to an example embodiment.

FIG. 5 is a schematic illustrating a conceptual partial view of anexample computer program product that includes a computer program forexecuting a computer process on a computing device.

FIG. 6 is a flow chart illustrating a method, according to an exampleembodiment.

FIG. 7 is a flow chart illustrating a method, according to an exampleembodiment.

DETAILED DESCRIPTION

Example methods and systems are described herein. Any example embodimentor feature described herein is not necessarily to be construed aspreferred or advantageous over other embodiments or features. Theexample embodiments described herein are not meant to be limiting. Itwill be readily understood that certain aspects of the disclosed systemsand methods can be arranged and combined in a wide variety of differentconfigurations, all of which are contemplated herein.

Furthermore, the particular arrangements shown in the Figures should notbe viewed as limiting. It should be understood that other embodimentsmay include more or less of each element shown in a given Figure.Further, some of the illustrated elements may be combined or omitted.Yet further, an example embodiment may include elements that are notillustrated in the Figures.

Example embodiments may relate to an autonomous vehicle, such as adriverless automobile, that is configured for active sensing. Inparticular, an autonomous vehicle may include various sensors, whichprovide information that the autonomous vehicle may use for variouscontrol processes. By implementing active-sensing processes, anautonomous vehicle may take proactive actions (referred to herein as“active-sensing actions”) in order to improve the information that isprovided by or derived from its various sensors, in a manner that is inturn expected to improve at least one of the vehicle's controlprocesses. Thus, active sensing may improve an autonomous vehicle'sability to, e.g., learn about its environment and/or performself-diagnosis.

However, when an autonomous vehicle takes an active-sensing action,there may be negative consequences. For instance, taking anactive-sensing action may increase the vehicle's chance of getting intoan accident, make a planned route more difficult, result in loss ofinformation from sensors that might be useful for purposes other thanthe control process that is being improved, annoy a passenger in thevehicle or a driver of another vehicle, and so on. Accordingly, exampleembodiments may help an autonomous vehicle consider and weigh both theinformation that might be gained by taking various active-sensingactions and the negative costs associated with those active-sensingactions, and determine what active-sensing action or action areappropriate to take (if any).

For example, to evaluate a given active-sensing action, an autonomousvehicle may determine an information-improvement value, which quantifiesthe information it expects to gain in the vehicle state that resultsfrom the action. The autonomous vehicle also determines a risk costassociated with the resulting vehicle state, which can be based on theprobability of certain negative scenario(s) occurring while in the newvehicle state. Further, in an example embodiment, theinformation-improvement expectation and the risk cost may be calculatedin the same unit of measure, so that a single value may be used toevaluate whether to take an action. As such, the autonomous vehicle candetermine a score for the active-sensing action, which takes intoaccount both the information-improvement expectation and the risk cost,and use the score to determine whether the active-sensing action isadvisable.

To illustrate, consider a possible active-sensing action where anautonomous vehicle can switch lanes to, e.g., allow its camera(s) orother sensors to obtain more information regarding a traffic light. Theautonomous car may determine that it could get a clearer or lessambiguous view of the traffic light by changing lanes, so that it is notblocked by a large SUV, is closer to the light, etc. Based on historicaland/or learnt data regarding the potential information gain when such anaction is taken, the autonomous car may determine aninformation-improvement value, which indicates how much information itexpects to learn by changing lanes. Further, the autonomous car maydetermine a risk cost for changing lanes, which may take into accountrisks such as running off the road, hitting a pedestrian (e.g., 0.1%chance of hitting a pedestrian in the lane closest to the curb versus a0.07% chance in the lane closest to the middle of the road), having anaccident with another car, annoying passenger(s) in the autonomous carand/or other cars resulting by changing lanes, and so on. A combinationof the information-improvement expectation and the risk cost may then beused to determine whether or not to switch lanes.

While the example above illustrates the application of an exampleprocess to evaluate a single action (i.e., changing from one lane toanother), an example process can also help to concurrently analyzemultiple active-sensing actions that are possible in a given state ofthe vehicle. Further, because information-improvement values and/or riskcosts for a given action may vary as an autonomous vehicle's environmentchanges over time, an autonomous vehicle may implement such a processperiodically or continually in order to base its decision-making on thecurrent state of its environment and/or its current operating state.

Note that in some embodiments, the risk cost for an action may bedetermined for the state that is expected to result from anactive-sensing action, in isolation. For instance, the risk cost for anaction that moves an autonomous vehicle into a certain lane may becalculated based solely on the probabilities of bad events occurringwhile the autonomous vehicle is in that lane. In other embodiments, therisk cost for an action may be relative, and based on a differencebetween the risk costs of the resulting state and its current state. Forexample, the risk cost for changing lanes may be the difference betweenthe risk cost in the lane the autonomous vehicle would be moving intoand the risk cost in its current lane.

Some example methods may be carried out in whole or in part by anautonomous vehicle or components thereof. However, some example methodsmay also be carried out in whole or in part by a system or systems thatare remote from an autonomous vehicle. For instance, an example methodcould be carried out in part or in full by a server system, whichreceives information from sensors (e.g., raw sensor data and/orinformation derived therefrom) of an autonomous vehicle. Other examplesare also possible.

Example systems within the scope of the present disclosure will now bedescribed in greater detail. An example system may be implemented in ormay take the form of an automobile. However, an example system may alsobe implemented in or take the form of other vehicles, such as cars,trucks, motorcycles, buses, boats, airplanes, helicopters, lawn mowers,earth movers, boats, snowmobiles, aircraft, recreational vehicles,amusement park vehicles, farm equipment, construction equipment, trams,golf carts, trains, and trolleys. Other vehicles are possible as well.

FIG. 1 is a functional block diagram illustrating a vehicle 100,according to an example embodiment. The vehicle 100 could be configuredto operate fully or partially in an autonomous mode, and thus may bereferred to as an “autonomous vehicle.” For example, a computer systemcould control the vehicle 100 while in the autonomous mode, and may beoperable to receive information from one or more sensors, upon which oneor more control processes for an autonomous vehicle are based, todetermine an information-improvement expectation that corresponds to anactive-sensing action, to determine a risk cost that corresponds to theactive-sensing action, and, based on both (i) theinformation-improvement expectation for the active-sensing action and(ii) the risk cost for the active-sensing action, to determine whetherthe active-sensing action is advisable.

It should be understood that an autonomous vehicle may be fullyautonomous or partially autonomous. In a partially autonomous vehiclesome functions may be manually controlled (e.g., by a person) some orall of the time. Further, a partially autonomous vehicle could beconfigured to switch between a fully-manual operation mode and apartially- or fully-autonomous operation mode.

The vehicle 100 could include various subsystems such as a propulsionsystem 102, a sensor system 104, a control system 106, one or moreperipherals 108, as well as a power supply 110, a computer system 112,and a user interface 116. The vehicle 100 may include more or fewersubsystems and each subsystem could include multiple elements. Further,each of the subsystems and elements of vehicle 100 could beinterconnected. Thus, one or more of the described functions of thevehicle 100 may be divided up into additional functional or physicalcomponents, or combined into fewer functional or physical components. Insome further examples, additional functional and/or physical componentsmay be added to the examples illustrated by FIG. 1.

The propulsion system 102 may include components operable to providepowered motion for the vehicle 100. Depending upon the embodiment, thepropulsion system 102 could include an engine/motor 118, an energysource 119, a transmission 120, and wheels/tires 121. The engine/motor118 could be any combination of an internal combustion engine, anelectric motor, steam engine, Stirling engine, or other types of enginesand/or motors. In some embodiments, the engine/motor 118 may beconfigured to convert energy source 119 into mechanical energy. In someembodiments, the propulsion system 102 could include multiple types ofengines and/or motors. For instance, a gas-electric hybrid car couldinclude a gasoline engine and an electric motor. Other examples arepossible.

The energy source 119 could represent a source of energy that may, infull or in part, power the engine/motor 118. That is, the engine/motor118 could be configured to convert the energy source 119 into mechanicalenergy. Examples of energy sources 119 include gasoline, diesel, otherpetroleum-based fuels, propane, other compressed gas-based fuels,ethanol, solar panels, batteries, and other sources of electrical power.The energy source(s) 119 could additionally or alternatively include anycombination of fuel tanks, batteries, capacitors, and/or flywheels. Theenergy source 119 could also provide energy for other systems of thevehicle 100.

The transmission 120 could include elements that are operable totransmit mechanical power from the engine/motor 118 to the wheels/tires121. The transmission 120 could include a gearbox, a clutch, adifferential, and a drive shaft. Other components of transmission 120are possible. The drive shafts could include one or more axles thatcould be coupled to the one or more wheels/tires 121.

The wheels/tires 121 of vehicle 100 could be configured in variousformats, including a unicycle, bicycle/motorcycle, tricycle, orcar/truck four-wheel format. Other wheel/tire geometries are possible,such as those including six or more wheels. Any combination of thewheels/tires 121 of vehicle 100 may be operable to rotate differentiallywith respect to other wheels/tires 121. The wheels/tires 121 couldrepresent at least one wheel that is fixedly attached to thetransmission 120 and at least one tire coupled to a rim of the wheelthat could make contact with the driving surface. The wheels/tires 121could include any combination of metal and rubber, and/or othermaterials or combination of materials.

The sensor system 104 may include a number of sensors configured tosense information about an environment of the vehicle 100. For example,the sensor system 104 could include a Global Positioning System (GPS)122, an inertial measurement unit (IMU) 124, a RADAR unit 126, a laserrangefinder/LIDAR unit 128, at least one camera 130, and/or at least onemicrophone 131. The sensor system 104 could also include sensorsconfigured to monitor internal systems of the vehicle 100 (e.g., O₂monitor, fuel gauge, engine oil temperature). Other sensors are possibleas well.

One or more of the sensors included in sensor system 104 could beconfigured to be actuated separately and/or collectively in order tomodify a position and/or an orientation of the one or more sensors.

The GPS 122 may be any sensor configured to estimate a geographiclocation of the vehicle 100. To this end, GPS 122 could include atransceiver operable to provide information regarding the position ofthe vehicle 100 with respect to the Earth.

The IMU 124 could include any combination of sensors (e.g.,accelerometers and gyroscopes) configured to sense position andorientation changes of the vehicle 100 based on inertial acceleration.

The RADAR unit 126 may represent a system that utilizes radio signals tosense objects within the local environment of the vehicle 100. In someembodiments, in addition to sensing the objects, the RADAR unit 126 mayadditionally be configured to sense the speed and/or heading of theobjects.

Similarly, the laser rangefinder or LIDAR unit 128 may be any sensorconfigured to sense objects in the environment in which the vehicle 100is located using lasers. Depending upon the embodiment, the laserrangefinder/LIDAR unit 128 could include one or more laser sources, alaser scanner, and one or more detectors, among other system components.The laser rangefinder/LIDAR unit 128 could be configured to operate in acoherent (e.g., using heterodyne detection) or an incoherent detectionmode.

The camera 130 could include one or more devices configured to capture aplurality of images of the environment of the vehicle 100. The camera130 could be a still camera or a video camera. In some embodiments, thecamera 130 may be mechanically movable such as by rotating and/ortilting a platform to which the camera is mounted. As such, a controlprocess of vehicle 100 may be implemented to control the movement ofcamera 130.

The sensor system 104 may also include a microphone 131. The microphone131 may be configured to capture sound in the environment of vehicle100. Further, in some implementations, a vehicle 100 may includemultiple be configured to capture sound from the environment of vehicle100. In some cases, multiple microphones may be arranged as a microphonearray, or possibly as multiple microphone arrays.

The control system 106 may be configured to control operation of thevehicle 100 and its components. Accordingly, the control system 106could include various elements include steering unit 132, throttle 134,brake unit 136, a sensor fusion algorithm 138, a computer vision system140, a navigation/pathing system 142, and an obstacle avoidance system144.

The steering unit 132 could represent any combination of mechanisms thatmay be operable to adjust the heading of vehicle 100.

The throttle 134 could be configured to control, for instance, theoperating speed of the engine/motor 118 and, in turn, control the speedof the vehicle 100.

The brake unit 136 could include any combination of mechanismsconfigured to decelerate the vehicle 100. The brake unit 136 could usefriction to slow the wheels/tires 121. In other embodiments, the brakeunit 136 could convert the kinetic energy of the wheels/tires 121 toelectric current. The brake unit 136 may take other forms as well.

The sensor fusion algorithm 138 may be an algorithm (or a computerprogram product storing an algorithm) configured to accept data from thesensor system 104 as an input. The data may include, for example, datarepresenting information sensed at the sensors of the sensor system 104.The sensor fusion algorithm 138 could include, for instance, a Kalmanfilter, Bayesian network, or other algorithm. The sensor fusionalgorithm 138 could further provide various assessments based on thedata from sensor system 104. Depending upon the embodiment, theassessments could include evaluations of individual objects and/orfeatures in the environment of vehicle 100, evaluation of a particularsituation, and/or evaluate possible impacts based on the particularsituation. Other assessments are possible.

The computer vision system 140 may be any system operable to process andanalyze images captured by camera 130 in order to identify objectsand/or features in the environment of vehicle 100 that could includetraffic signals, road way boundaries, and obstacles. The computer visionsystem 140 could use an object recognition algorithm, a Structure FromMotion (SFM) algorithm, video tracking, and other computer visiontechniques. In some embodiments, the computer vision system 140 could beadditionally configured to map an environment, track objects, estimatethe speed of objects, etc.

The navigation and pathing system 142 may be any system configured todetermine a driving path for the vehicle 100. The navigation and pathingsystem 142 may additionally be configured to update the driving pathdynamically while the vehicle 100 is in operation. In some embodiments,the navigation and pathing system 142 could be configured to incorporatedata from the sensor fusion algorithm 138, the GPS 122, and one or morepredetermined maps so as to determine the driving path for vehicle 100.

The obstacle avoidance system 144 could represent a control systemconfigured to identify, evaluate, and avoid or otherwise negotiatepotential obstacles in the environment of the vehicle 100.

The control system 106 may additionally or alternatively includecomponents other than those shown and described.

Peripherals 108 may be configured to allow interaction between thevehicle 100 and external sensors, other vehicles, other computersystems, and/or a user. For example, peripherals 108 could include awireless communication system 146, a touchscreen 148, a microphone 150,and/or a speaker 152.

In an example embodiment, the peripherals 108 could provide, forinstance, means for a user of the vehicle 100 to interact with the userinterface 116. To this end, the touchscreen 148 could provideinformation to a user of vehicle 100. The user interface 116 could alsobe operable to accept input from the user via the touchscreen 148. Thetouchscreen 148 may be configured to sense at least one of a positionand a movement of a user's finger via capacitive sensing, resistancesensing, or a surface acoustic wave process, among other possibilities.The touchscreen 148 may be capable of sensing finger movement in adirection parallel or planar to the touchscreen surface, in a directionnormal to the touchscreen surface, or both, and may also be capable ofsensing a level of pressure applied to the touchscreen surface. Thetouchscreen 148 may be formed of one or more translucent or transparentinsulating layers and one or more translucent or transparent conductinglayers. The touchscreen 148 may take other forms as well.

In other instances, the peripherals 108 may provide means for thevehicle 100 to communicate with devices within its environment. Themicrophone 150 may be configured to receive audio (e.g., a voice commandor other audio input) from a user of the vehicle 100. Similarly, thespeakers 152 may be configured to output audio to the user of thevehicle 100.

In one example, the wireless communication system 146 could beconfigured to wirelessly communicate with one or more devices directlyor via a communication network. For example, wireless communicationsystem 146 could use 3G cellular communication, such as CDMA, EVDO,GSM/GPRS, or 4G cellular communication, such as WiMAX or LTE.Alternatively, wireless communication system 146 could communicate witha wireless local area network (WLAN), for example, using WiFi. In someembodiments, wireless communication system 146 could communicatedirectly with a device, for example, using an infrared link, Bluetooth,or ZigBee. Other wireless protocols, such as various vehicularcommunication systems, are possible within the context of thedisclosure. For example, the wireless communication system 146 couldinclude one or more dedicated short range communications (DSRC) devicesthat could include public and/or private data communications betweenvehicles and/or roadside stations.

The power supply 110 may provide power to various components of vehicle100 and could represent, for example, a rechargeable lithium-ion orlead-acid battery. In some embodiments, one or more banks of suchbatteries could be configured to provide electrical power. Other powersupply materials and configurations are possible. In some embodiments,the power supply 110 and energy source 119 could be implementedtogether, as in some all-electric cars.

Many or all of the functions of vehicle 100 could be controlled bycomputer system 112. Computer system 112 may include at least oneprocessor 113 (which could include at least one microprocessor) thatexecutes instructions 115 stored in a non-transitory computer readablemedium, such as the data storage 114. The computer system 112 may alsorepresent a plurality of computing devices that may serve to controlindividual components or subsystems of the vehicle 100 in a distributedfashion.

In some embodiments, data storage 114 may contain instructions 115(e.g., program logic) executable by the processor 113 to execute variousfunctions of vehicle 100, including those described above in connectionwith FIG. 1. Data storage 114 may contain additional instructions aswell, including instructions to transmit data to, receive data from,interact with, and/or control one or more of the propulsion system 102,the sensor system 104, the control system 106, and the peripherals 108.

In addition to the instructions 115, the data storage 114 may store datasuch as roadway maps, path information, among other information. Suchinformation may be used by vehicle 100 and computer system 112 at duringthe operation of the vehicle 100 in the autonomous, semi-autonomous,and/or manual modes.

The vehicle 100 may include a user interface 116 for providinginformation to or receiving input from a user of vehicle 100. The userinterface 116 could control or enable control of content and/or thelayout of interactive images that could be displayed on the touchscreen148. Further, the user interface 116 could include one or moreinput/output devices within the set of peripherals 108, such as thewireless communication system 146, the touchscreen 148, the microphone150, and the speaker 152.

The computer system 112 may control the function of the vehicle 100based on inputs received from various subsystems (e.g., propulsionsystem 102, sensor system 104, and control system 106), as well as fromthe user interface 116. For example, the computer system 112 may utilizeinput from the control system 106 in order to control the steering unit132 to avoid an obstacle detected by the sensor system 104 and theobstacle avoidance system 144. Depending upon the embodiment, thecomputer system 112 could be operable to provide control over manyaspects of the vehicle 100 and its subsystems.

The components of vehicle 100 could be configured to work in aninterconnected fashion with other components within or outside theirrespective systems. For instance, in an example embodiment, the camera130 could capture a plurality of images that could represent informationabout a state of an environment of the vehicle 100 operating in anautonomous mode. The environment could include other vehicles, trafficlights, traffic signs, road markers, pedestrians, etc. The computervision system 140 could recognize the various aspects of the environmentbased on object recognition models stored in data storage 114, or byusing other techniques.

Although FIG. 1 shows various components of vehicle 100, i.e., wirelesscommunication system 146, computer system 112, data storage 114, anduser interface 116, as being integrated into the vehicle 100, one ormore of these components could be mounted or associated separately fromthe vehicle 100. For example, data storage 114 could, in part or infull, exist separate from the vehicle 100. Thus, the vehicle 100 couldbe provided in the form of device elements that may be locatedseparately or together. The device elements that make up vehicle 100could be communicatively coupled together in a wired and/or wirelessfashion.

FIG. 2 shows a vehicle 200 that could be similar or identical to vehicle100 described in reference to FIG. 1. Although vehicle 200 isillustrated in FIG. 2 as a car, other embodiments are possible. Forinstance, the vehicle 200 could represent a truck, a van, a semi-trailertruck, a motorcycle, a golf cart, an off-road vehicle, or a farmvehicle, among other examples.

Depending on the embodiment, vehicle 200 could include a sensor unit202, a wireless communication system 204, a LIDAR unit 206, a laserrangefinder unit 208, and a camera 210. The elements of vehicle 200could include some or all of the elements described for FIG. 1.

The sensor unit 202 could include one or more different sensorsconfigured to capture information about an environment of the vehicle200. For example, sensor unit 202 could include any combination ofcameras, RADARs, LIDARs, range finders, and acoustic sensors. Othertypes of sensors are possible. Depending on the embodiment, the sensorunit 202 could include one or more movable mounts that could be operableto adjust the orientation of one or more sensors in the sensor unit 202.In one embodiment, the movable mount could include a rotating platformthat could scan sensors so as to obtain information from each directionaround the vehicle 200. In another embodiment, the movable mount of thesensor unit 202 could be moveable in a scanning fashion within aparticular range of angles and/or azimuths. The sensor unit 202 could bemounted atop the roof of a car, for instance, however other mountinglocations are possible. Additionally, the sensors of sensor unit 202could be distributed in different locations and need not be collocatedin a single location. Some possible sensor types and mounting locationsinclude LIDAR unit 206 and laser rangefinder unit 208. Furthermore, eachsensor of sensor unit 202 could be configured to be moved or scannedindependently of other sensors of sensor unit 202.

The wireless communication system 204 could be located on a roof of thevehicle 200 as depicted in FIG. 2. Alternatively, the wirelesscommunication system 204 could be located, fully or in part, elsewhere.The wireless communication system 204 may include wireless transmittersand receivers that could be configured to communicate with devicesexternal or internal to the vehicle 200. Specifically, the wirelesscommunication system 204 could include transceivers configured tocommunicate with other vehicles and/or computing devices, for instance,in a vehicular communication system or a roadway station. Examples ofsuch vehicular communication systems include dedicated short rangecommunications (DSRC), radio frequency identification (RFID), and otherproposed communication standards directed towards intelligent transportsystems.

The camera 210 may be any camera (e.g., a still camera, a video camera,etc.) configured to capture a plurality of images of the environment ofthe vehicle 200. To this end, the camera 210 may be configured to detectvisible light, or may be configured to detect light from other portionsof the spectrum, such as infrared or ultraviolet light. Other types ofcameras are possible as well.

The camera 210 may be a two-dimensional detector, or may have athree-dimensional spatial range. In some embodiments, the camera 210 maybe, for example, a range detector configured to generate atwo-dimensional image indicating a distance from the camera 210 to anumber of points in the environment. To this end, the camera 210 may useone or more range detecting techniques.

For example, the camera 210 may use a structured light technique inwhich the vehicle 200 illuminates an object in the environment with apredetermined light pattern, such as a grid or checkerboard pattern anduses the camera 210 to detect a reflection of the predetermined lightpattern off the object. Based on distortions in the reflected lightpattern, the vehicle 200 may determine the distance to the points on theobject. The predetermined light pattern may comprise infrared light, orlight of another wavelength.

As another example, the camera 210 may use a laser scanning technique inwhich the vehicle 200 emits a laser and scans across a number of pointson an object in the environment. While scanning the object, the vehicle200 uses the camera 210 to detect a reflection of the laser off theobject for each point. Based on a length of time it takes the laser toreflect off the object at each point, the vehicle 200 may determine thedistance to the points on the object.

As yet another example, the camera 210 may use a time-of-flighttechnique in which the vehicle 200 emits a light pulse and uses thecamera 210 to detect a reflection of the light pulse off an object at anumber of points on the object. In particular, the camera 210 mayinclude a number of pixels, and each pixel may detect the reflection ofthe light pulse from a point on the object. Based on a length of time ittakes the light pulse to reflect off the object at each point, thevehicle 200 may determine the distance to the points on the object. Thelight pulse may be a laser pulse. Other range detecting techniques arepossible as well, including stereo triangulation, sheet-of-lighttriangulation, interferometry, and coded aperture techniques, amongothers. The camera 210 may take other forms as well.

The camera 210 could be mounted inside a front windshield of the vehicle200. Specifically, as illustrated, the camera 210 could capture imagesfrom a forward-looking view with respect to the vehicle 200. Othermounting locations and viewing angles of camera 210 are possible, eitherinside or outside the vehicle 200.

The camera 210 could have associated optics that could be operable toprovide an adjustable field of view. Further, the camera 210 could bemounted to vehicle 200 with a movable mount that could be operable tovary a pointing angle of the camera 210.

FIG. 3 is a flow chart illustrating a method 300, according to anembodiment. Method 300 is described by way of example as being carriedout by an autonomous vehicle. In particular, method 300 may be carriedout by a system or subsystem of an autonomous vehicle, such as any ofthe features of vehicle shown in FIGS. 1 and 2 and described above.However, other systems and configurations could be used. Further, whileFIG. 3 illustrates functions in an example method, it should beunderstood that in other embodiments, functions may appear in differentorder, may be added, and/or may be subtracted.

As shown by block 302, method 300 involves an autonomous vehiclereceiving information from one or more sensors of an autonomous vehicle,where one or more of control processes for the autonomous vehicle arebased upon the information. The autonomous vehicle then determines aninformation-improvement expectation that corresponds to anactive-sensing action, where the active-sensing action is performable bythe autonomous vehicle to potentially improve the information upon whichat least one control process for the autonomous vehicle is based, asshown by block 304. The autonomous vehicle also determines a risk costthat corresponds to the active-sensing action, as shown by block 306.The autonomous vehicle may then determine, based on both (i) theinformation-improvement expectation for the active-sensing action and(ii) the risk cost for the active-sensing action, whether theactive-sensing action is advisable, as shown by block 308. Further, insome embodiments, method 300 may involve the autonomous vehicleinitiating and/or performing the active-sensing action.

Note that information may be received from sensors in various ways. Insome cases, the raw sensor data itself may provide information uponwhich a control process is based. In other cases, control processes maybe based on information that is learned or derived from analysis and/orprocessing of sensor data. The information that is received from asensor may take other forms as well.

Method 300 may be implemented to determine whether various types ofactive-sensing actions are advisable. As an example, method 300 may beimplemented to evaluate a movement of the autonomous vehicle from afirst lane to a second lane that is taken in an effort to acquire moreinformation via the sensors of the autonomous vehicle. For instance, anautonomous vehicle could apply method 300 to evaluate whether to movefrom the middle lane on the highway to a passing lane, or whether tomove from the right-hand lane to a left-hand lane on a two-laneexpressway. Other examples of lane-switching active-sensing actions arepossible as well. Further, an active-sensing action might involvechanging position within a lane to, e.g., be closer to one edge of thelane or closer to a curb.

Method 300 could also be implemented to evaluate an active-sensingaction that involves a change in speed of the autonomous vehicle. Forexample, an autonomous vehicle could determine whether it is advisablefor it to speed up or slow down.

Further, method 300 could be implemented to evaluate an active-sensingaction that involves a change in position of the autonomous vehiclerelative to an aspect of an environment of the autonomous vehicle. Forinstance, an active-sensing action might involve speeding up to passand/or pull in front of another vehicle on the road. As another example,an active-sensing action might involve changing lanes to get a betterview of a traffic light. Many other examples are possible.

Yet further, method 300 could be implemented to evaluate anactive-sensing action that involves a change in position of at least oneof the sensors that provides information for at least one controlprocess. For example, if an autonomous vehicle includes a camera oranother sensor that mechanically moveable, an active-sensing actioncould involve moving the camera to change the camera's field of view.Other examples of moving a camera and/or other sensors are possible.

Additionally or alternatively, method 300 could be implemented toevaluate an active-sensing action that involves a change in operation ofat least one of the sensors that provides the sensor data for the atleast one control process. For example, an active-sensing action mayinvolve causing a camera to zoom in on an aspect of the environment inan effort to acquire more information about the aspect.

Further, in some implementations, an active-sensing may involve a changeto the processing of sensor data from at least one sensor. As examples,the autonomous vehicle may apply a different filter to sensor data, oradjust a parameter that affects the manner in which sensor data isanalyzed or evaluated. Other examples are also possible.

Furthermore, it should be understood that an active-sensing action mayinvolve a single action or a combination of multiple actions. Forexample, in order to acquire more information about an aspect of itsenvironment, an autonomous vehicle might simply change lanes. However,an active sensing action could also involve the autonomous vehiclechanging lanes, moving forward a certain distance, and rotating a cameratowards an aspect of its environment. Many other examples ofactive-sensing actions involving a single action and combinations ofmultiple actions are possible.

It should be understood that the above-described examples ofactive-sensing actions are not intended to be limiting. A method, suchas method 300, may be used to evaluate any active-sensing action that isperformable to potentially improve information upon which at least onecontrol process for an autonomous vehicle is based.

At block 304 of method 300, the information-improvement expectation maybe indicative of an expected improvement to the control process, if theactive-sensing action is taken. In particular, theinformation-improvement expectation may indicate an expected improvementto the information provided by sensor data, for purposes of one or morecontrol processes, as a result of the autonomous vehicle performing theactive-sensing action.

To provide an example, an information-improvement expectation for agiven active-sensing action may quantify an improvement to theinformation that images from a camera can provide about the environment,which in turn is expected to improve a control process. For example, ifan autonomous vehicle is driving side-by-side with another vehicle forsome period of time, its camera's view of the side of the road may havebeen obstructed, and the autonomous vehicle may not have detected aspeed-limit sign for some period. As such, the autonomous vehicle maydetermine an information-improvement expectation that numericallyrepresents the information it expects to gain (e.g., knowledge of thespeed limit) if it speeds up or slows down so the other vehicle is nolonger between it and the side of the road.

For example, consider a scenario where improvement of one or morelocation-based control processes is expected if a better GPS signal canbe obtained. In this scenario, an information-improvement expectationmay quantify the expected improvement to the location-based controlprocesses from an active-sensing action that is expected to provide aclearer view of a satellite, such that a better GPS signal can bereceived. Many other types of information gains, for purposes of manytypes of control processes, may also be captured in aninformation-improvement expectation.

At block 304, various techniques may be used to determine theinformation-improvement expectation. For example, the autonomous vehiclemay determine an information value provided by sensor data in a firststate of the autonomous vehicle (e.g., its current state), as well as anexpected information value that is expected to be provided by sensordata in a second state of the autonomous vehicle (e.g., the state thatis expected if the autonomous vehicle were to perform the active-sensingaction.) The autonomous vehicle can then subtract the information valuefrom the expected information value to determine theinformation-improvement expectation.

In some implementations, there may be multiple improvements that couldpotentially result from the active-sensing action. Accordingly, theinformation-improvement expectation for the active-sensing action mayaccount for some or all of the potential improvements.

For example, the autonomous vehicle may determine aninformation-improvement value for each improvement to the informationupon which the at least one of the control process is based. To do so,the autonomous vehicle may determine an information value that isassociated with a first state of the autonomous vehicle (e.g., itscurrent state), as well as an expected information value that isassociated with a second state of the autonomous vehicle (e.g., thestate that is expected if the autonomous vehicle were to perform theactive-sensing action.) The autonomous vehicle can then subtract theinformation value from the expected information value to determine theinformation-improvement value.

In addition, the autonomous vehicle may determine the probability ofeach improvement occurring as a result of the autonomous vehicleperforming the active-sensing action. Then, for each improvement,autonomous vehicle can then multiply the information value of theimprovement by the probability of the improvement occurring to determinean expected information value for the improvement. Further, in thescenario where there are multiple potential improvements as a result ofan active-sensing action, the autonomous vehicle may determine theinformation-improvement expectation for the active-sensing action bysumming the expected information values for some or all of the potentialimprovements.

At block 306, various techniques may be used to determine the risk costthat corresponds to the active-sensing action. In an example embodiment,the autonomous vehicle may evaluate the individual risk costs, or therisk penalties, for various bad events that might occur as the result ofperforming an active-sensing action. In particular, the risk penalty fora given bad event may be determined based on the probability of the badevent occurring and the risk magnitude of the bad event (e.g., anumerical representation of the bad event's severity).

Various bad events may be considered when determining the risk cost foran active-sensing action. For example, bad events may include varioustypes of accidents, such as an accident with another vehicle, anaccident with a police car or ambulance (for which the risk magnitudemay be greater than it is for accidents with other vehicles), and/or anaccident involving a pedestrian, among other possibilities.

Further, bad events are not limited to accidents, and may include anyevent that could be perceived as negative in some way. For instance, anautonomous vehicle may consider the dislike of an active-sensing actionby a passenger in an autonomous vehicle to be a bad event (e.g., if anactive-sensing action results in the autonomous vehicle repeatedlyswitching back and forth between lanes, or suddenly accelerating orcoming to a stop). Similarly, the dislike or annoyance of passengers inother vehicles due to an active-sensing action may be considered a badevent, which may be taken into account when determining the risk costfor the active-sensing action.

Yet further, if an active-sensing action could make a future action moredifficult, such as a planned turn or exit from a highway, this may beclassified as a bad event for the active-sensing action. Possible futureactions of other vehicle that are responsive to an active-sensingaction, and are considered undesirable, can also be classified as badevents associated with the active-sensing action.

Even further, while an active-sensing action may often be taken in orderto gain information from sensor data, other information from sensor datamay be lost in the process. Accordingly, the expected degradation to acontrol process corresponding to information lost as the result of anactive-sensing action, may also be considered a bad event.

In some implementations, the time it takes to evaluate an active-sensingaction may be considered a bad event. In particular, a risk penalty maybe calculated for the processing time it takes to determine and weightthe information-improvement expectation and/or the risk cost. Othertypes of bad events are also possible as well.

To calculate the risk penalty for a given bad event, the autonomousvehicle may determine a probability of the bad event occurring as aresult of the autonomous vehicle performing the active-sensing action.The autonomous vehicle may also determine a risk magnitude thatcorresponds to a severity of the bad event, if it does in fact occur.The autonomous vehicle can then use the respective probabilities and therespective risk magnitudes for the bad events to determine therespective risk penalties for the bad events. The autonomous vehicle maythen sum the risk penalties of the potential bad events to determine therisk cost for the active-sensing action. Other techniques fordetermining the risk cost of performing an active-sensing action arepossible as well.

Note that in some implementations, risk cost could be determined inabsolute manner; e.g., by quantifying risks in the vehicle state that isexpected to result from the active sensing action. For example, couldsimply add up the risk-contributions of the various bad events that areevaluated.

In other implementations, the risk cost could be evaluated relative to acurrent state of an autonomous vehicle. As such, the autonomous vehiclemay determine the risk cost associated with a second (e.g., expected)state that is expected to result from the active sensing action, as wellas the risk cost associated with the current state of the autonomousvehicle. The risk cost for the active-sensing action may then becalculated by subtracting the risk cost for the expected state from therisk cost for the current state.

At block 308, the autonomous vehicle may use various techniques todetermine whether an active-sensing action is advisable. In an exampleembodiment, the information-improvement expectation and the risk costmay be determined in the same unit of measure. However, it should beunderstood that the information-improvement expectation and the riskcost may be determined in different units of measure, without departingfrom the scope of the invention.

In some implementations, the autonomous vehicle may determine a scorefor the active-sensing action. The score may be based on both (i) theinformation-improvement expectation and (ii) the risk cost. For example,the autonomous vehicle may determine the score for an active-sensingaction by subtracting the risk cost for the active-sensing action fromthe information-improvement expectation for the active-sensing action.

Once the autonomous vehicle has determined the score, various techniquesmay be used to determine whether the score is indicative of an advisableactive-sensing action. For example, the autonomous vehicle may determinethat an active-sensing action is advisable when its score is above athreshold. Other techniques for determining whether active-sensingaction is advisable are also possible.

For instance, an example method may be used to concurrently evaluate anumber of possible active-sensing actions. In such an embodiment, theautonomous vehicle may evaluate a number of active-sensing actions andselect a single action that is advisable. To do so, the autonomousvehicle may evaluate information-improvement expectations and risk costin the expected vehicle state corresponding to each possibleactive-sensing action. The autonomous vehicle may then determine a scorefor each active-sensing action by subtracting the respective risk costfrom the respective information-improvement expectation. The autonomousvehicle could then perform the top-scoring active-sensing action(perhaps requiring some threshold score in order to do so).

In some implementations, the autonomous vehicle could determine thatmultiple active-sensing actions are advisable. To do so, the autonomousvehicle could determine a score for each possible active-sensing actionas described above, and then classify active-sensing actions havinggreater than a threshold score as advisable. Alternatively, theautonomous vehicle could rank the possible active-sensing actionsaccording to their scores. The autonomous vehicle could then determine acertain number of the highest-ranking active-sensing actions to beadvisable and/or could perform these active-sensing actions.

In a further aspect, an autonomous vehicle may implement method 100 in aperiodic or continual manner. This may be beneficial since an autonomousvehicle's state may change continually, as it moves or takes otheractions, and/or as its environment changes. These changes in the vehiclestate and/or to environment typically may result in change to theactive-sensing actions that are possible, change to theinformation-improvement expectation for a given active-sensing action,and/or change to the risk cost for a given active-sensing action. Thus,by implementing method 100 periodically or continually for a number ofpotential active-sensing actions, the autonomous vehicle may be able toperiodically or continually update the active-sensing actions that arepossible in its current state and environment, and weigh the knowledgeimprovement from the possible active-sensing actions against the riskcosts associated with those active-sensing actions.

FIG. 4 is a top-down view of an autonomous vehicle operating scenario400, according to an example embodiment. In scenario 400, an autonomousvehicle 402 may carry out a method, such as method 300, in order todetermine whether or not to perform an active-sensing action. Theautonomous vehicle 402 (which may also be referred to simply as vehicle402) may be identical or similar to vehicle 200 of FIG. 2.Alternatively, vehicle 402 may be another type of autonomous vehicle.

Scenario 400 shows the autonomous vehicle 402 and a large truck 404 on aroadway that includes two lanes in either direction. Note, however, thatFIG. 4 shows only the right-hand lane 406 and the left-hand lane 408 inthe direction that vehicle 402 is travelling on the roadway. FIG. 4 alsoshows a traffic light 412 and a curb 414.

In scenario 400, vehicle 402 has come to a stop in the right-hand lane408 behind the large truck 404. As such, the sensor unit 410 of thevehicle 402 could be capturing sensor data based on an environment ofthe vehicle 402. For example, a camera could capture video and/or stillimages of the truck 404, the curb 414, and as well as other features inthe environment so as to help the computer system of the vehicle 402 todetermine the current state of the environment of the vehicle 402 (e.g.,dashed and solid lane markers defining the right-hand and left-handlanes 406 and 408). Other sensors associated with the vehicle 402 couldbe operable to provide the speed, heading, location, and other data suchthat the computer system of the vehicle 402 could determine the currentstate of the vehicle 402. However, in scenario 400, the large truck 404may partially or wholly prevent the camera or cameras of sensor unit 410from capturing image data that includes traffic light 412.

In a further aspect vehicle 402 may be configured for active sensing.Thus, for various reasons, vehicle 402 may determine that better imagedata of the traffic light 412 might improve its control processes. Forexample, various control processes may function according to the stateof the traffic light 412 (e.g., red, yellow, or green). Such controlprocesses may therefore be improved with, e.g., more complete images oftraffic light 412, which allow vehicle 402 to better discern when thetraffic light 412 switches between red, yellow, and green.

Since more complete images of traffic light 412 could improve one ormore of its control processes, vehicle 402 may determine anactive-sensing action that, if performed, could result in more completeimages of traffic light 412. For instance, vehicle 402 may determine itcould move into the left-hand lane 408 by e.g., moving along path 416,to the position illustrated by the dotted outline of vehicle 402, nextto the large truck 404. (For purposes of this example, the movement ofvehicle 402, along path 416, to the position illustrated by the dottedoutline of the vehicle, may also be referred to as the active-sensingaction 416.)

According to an example embodiment, vehicle 402 may then weigh thepotential improvement to its knowledge of its environment and/or itscontrol processes, against risk costs associated with taking the activesensing action. In particular, vehicle 402 may calculate aninformation-improvement expectation and a risk cost for switching lanesand pulling up beside large truck 404.

To calculate the information-improvement expectation, vehicle 402 maydetermine a value that is indicative of the expected improvement to theinformation provided by sensor data, for purposes of its controlprocesses. To facilitate such calculations, vehicle 402 may include orhave access to information-valuation data, which assigns certain valuesto certain types of information, for certain control processes.

To provide a specific example, information-valuation data may specifythat, for purposes of a control process that determines when toaccelerate the vehicle upon detecting a green light, knowing the stateof a traffic light has a value of 100. Thus, in some cases, theinformation-improvement expectation may simply be set to 100. In othercases, however, vehicle 402 may account for possible uncertainty as towhether and/or by how much moving into the left-hand lane 408 willimprove its knowledge of the state of traffic light 412. For example, ifvehicle 402 determines that there is a 90% probability of correctlydetermining the state of traffic light after moving along path 416 intothe left-hand lane 408, then it may weigh the value associated withgaining complete knowledge of the state of traffic light 412; e.g., bymultiplying the value of 100 by the 90% probability of correctlydetermining the state of traffic light from the left-hand lane 408, toarrive at an information-improvement expectation of 90.

Note that the information-improvement expectation may quantify and/or beindicative of risk reduction and/or an increased probability ofaccomplishing a certain goal or task (e.g., driving the car to a certainlocation), which is expected to result from taking a certainactive-sensing action. Accordingly, information-valuation data mayquantify and/or be indicative of risk reduction and/or an increasedprobability of accomplishing the goal or task, which certain pieces ofinformation are expected to provide. More specifically, the autonomousvehicle may be able to calculate a probability of accomplishing acertain goal or task given the processes, routines, controllers,sensors, current context, and/or currently-available information of theautonomous vehicle. Accordingly, the information-valuation data mayinclude an incremental information value for each of a number of piecesof information, which indicates the increase to the probability ofaccomplishing the goal and/or the reduction to the risks associated withaccomplishing the goal, which is expected to result if the piece ofinformation is acquired.

Further, since the information-improvement expectation and the risk costmay both be indicative of change to the risk associated withaccomplishing a goal and/or change to the probability of achieving agoal, both information-improvement expectation and the risk cost may becalculated in the same unit of measure. As such, theinformation-valuation data may include incremental information valuesfor each of a number of pieces of information, which are also in thesame unit of measure as the risk cost.

In some cases, vehicle 402 may also consider information that it alreadyhas, in its current state, when determining the information-improvementexpectation for an active-sensing action. In particular, vehicle 402 maydetermine an information value for the state that would be expected ifthe active-sensing action were performed. Vehicle 402 may then calculatethe information-improvement expectation by subtracting an informationvalue for its current state from the expected information value thatcorresponds to the state that is expected after performing theactive-sensing action.

As a specific example, in scenario 400, the traffic light 412 may onlybe partially obstructed by the large truck 404. Accordingly, based onits camera's partial view of traffic light 412, vehicle 402 maydetermine that there is a 60% probability of correctly determining thestate of traffic light 412, in its current position, and a 90%probability in the state that is expected if it were to perform theactive-sensing action. If information-valuation data assigns a value of100 to complete knowledge of a traffic light's state, then vehicle 402may determine the expected information value to be equal to 90, and itscurrent information value to be 60. Thus, vehicle 402 may calculate aninformation-improvement expectation of 30 for the active-sensing actionof moving into left-hand lane 408 along path 416.

In a further aspect, vehicle 402 may determine aninformation-improvement expectation that accounts for the possibility ofactive-sensing action 416 improving information for multiple controlprocesses. To do so, an information-improvement expectation may beindividually calculated with respect to each control process for whichinformation may be improved, and the individual information-improvementexpectations may be added together to determine theinformation-improvement expectation for the active-sensing action.

For example, vehicle 402 could also implement a control process thatestimates the length of a red light, moves a camera to gather otherenvironmental information, while the light is expected to be red, andthen moves the camera back to observe the light just before it expectsthe light to turn green. Further, information-valuation data may specifythat knowing the state of a traffic light has a value of 50 for purposesof this control process. Thus, continuing the above example where thereis a 30% increase to the probability of correctly determining the stateof the traffic light 412, the vehicle may calculate aninformation-improvement expectation of 15 for the active-sensing action,with respect to the control process that repurposes the camera during ared light. The information-improvement expectation for theactive-sensing action may then be calculated to be 45 by, e.g., summingthe information-improvement expectations to the two control processes.

In yet a further aspect, a vehicle 402 may consider the possibility ofan active-sensing action improving the information used for a singlecontrol process in more than one way. For example, moving along path 416might also improve the camera's ability to capture a street corner undertraffic light 412, which may be useful for control processes that searchfor pedestrians and/or for other control processes. Accordingly, vehicle402 may use similar techniques as described above to determineindividual information-improvement expectations for the controlprocesses that are expected to improve with better image data of thestreet corner. Vehicle 402 may then calculate theinformation-improvement expectation for the active-sensing action byadding these individual information-improvement expectations to theindividual information-improvement expectations associated with theimprovement in ability to determine the state of traffic light 412 (andpossibly to individual information-improvement expectations associatedwith other information gains expected to result from the active-sensingaction, as well).

To calculate the risk cost associated with the active-sensing action,vehicle 402 may determine a value that represents the chances of a badevent occurring as a result of a movement into the left-hand land 408along path 416. In an example, vehicle 402 may determine individual riskpenalties for a number of bad events that could potentially result fromthe active-sensing action, and calculate the risk cost for theactive-sensing action by summing the individual penalties of the badevents.

To calculate the individual risk costs, vehicle 402 may determine a riskmagnitude for each potential bad event, which indicates the severity ofthe bad event, if it were to occur. Vehicle 402 may also determine theprobability of the bad event occurring as a result of the active-sensingaction (e.g., during the performance of, or in the state of the vehiclethat results from performing the active-sensing action).

Table 1 provides some examples of bad events that vehicle 402 may takeinto consideration in scenario 400, when determining whether it isadvisable to move along path 416 into left-hand land 408.

TABLE 1 Risk Probability Risk Bad Event Magnitude (%) Penalty gettinghit by large truck 5,000 0.01% 0.5 getting hit by an oncoming 20,0000.01 2 vehicle getting hit from behind by 10,000 0.03% 3 vehicle (notshown) approaching in the left- hand lane 408 hitting pedestrian whoruns 100,000 0.001%  1 into the middle of the road losing informationthat is 10   10% 1 provided by camera in current position losinginformation that is 2   25% 0.5 provided by other sensor in currentposition Interference with path 50 100% (if turn is 50/0 planninginvolving right planned)/0% (if turn at traffic light 412 no turn isplanned)

As shown in Table 1, vehicle 402 may consider the risk of having anaccident with the large truck 404, the risk of an accident with anoncoming vehicle, and the risk of an accident with a vehicle (not shown)approaching from behind vehicle 402 in the left-hand lane 408. Since anaccident with another vehicle may be considered highly undesirable, therisk magnitudes for these bad events is relatively high; e.g., rangingfrom 5,000 to 20,000, depending on the expected severity of therespective type of accident. However, since large truck 404 is stoppedat the light, the probability of the large truck suddenly moving intothe left-hand lane 408 may be relatively low; e.g., 0.01%. Similarly,the probability of an oncoming vehicle suddenly swerving into theleft-hand lane 408 may be considered relatively low, e.g., 0.01%. Anaccident with a vehicle approaching from behind in the left-hand lane408 may be considered more probable, although still relatively low(assuming that sensor data has not detected a vehicle approaching).

Further, as shown in Table 1, vehicle 402 may consider the risk hittinga pedestrian that runs into the street. While the probability of thisoccurring may be determined to be very low (e.g., 0.001%), an accidentwith a pedestrian may be considered very undesirable, and worse than anaccident with another vehicle. Accordingly, the risk magnitude forhitting a pedestrian may be much higher than the risk magnitudes foraccidents with other vehicles.

As further shown in Table 1, vehicle 402 may consider the risk of losinginformation that is provided by camera and/or by other sensors incurrent position. More specifically, while the vehicle's camera may bebetter positioned to capture traffic light 412 if it moves along path416, it may be in a worse position for capturing other aspects of thevehicle's environment. Information provided by other sensors may also beless useful for various control processes, if the active-sensing actionis taken. Accordingly, the risk penalty for lost information may bedetermined by calculating an information-reduction expectation for theactive-sensing action. The information-reduction expectation, which isindicative of an expected worsening of one or more control processes dueto reduced information, may be determined using similar techniques asthose described for determining an information-improvement value, whichcorrespond to control-process improvements resulting from gains ininformation.

Additionally, vehicle 402 may consider the risk of it becoming moredifficult to carry out planned actions, if an active-sensing action isperformed. For instance, as shown in Table 1, vehicle 402 may considerwhether its path planning involves a right turn at traffic light 412. Asindicated, this risk may be evaluated in a binary manner, as the plannedpath of travel either involves a right turn or does not; i.e., theprobability that moving along path 416 into left-hand lane 408 will makeit more difficult to make a planned right turn is either 100%, if theturn is planned, or 0%, if no turn is planned. Further, if a turn isplanned, having to reroute may be considered to be fairly undesirable,and thus the risk magnitude and the risk penalty may be equal to 50.

As noted, to determine the risk cost for active-sensing action 416, thevehicle may sum the risk penalties associated with the bad events thatmight occur due to the active-sensing action. For example, consider thecase where risk penalties are determined as shown in Table 1, and noright turn is planned at traffic light 412 (meaning that the riskpenalty for interference with a planned right turn is 0). In this case,vehicle 402 may determine the risk cost for active-sensing action 416 tobe equal to 8. However, if a right turn is planned at traffic light 412,then the risk cost may be determined to be 58.

In the above examples, the risk cost for active-sensing action 416 isindicative of the risk level associated with bad events that arepossible in the vehicle state that is expected to result from the activesensing action and/or bad events that are possible while theactive-sensing action is being performed. However, in other examples,the risk cost may indicate the difference between the risk level in thevehicle's current state and the risk level that is expected to result ifthe active sensing action is performed.

For example, vehicle 402 may calculate a risk cost in its current state,with it located in the right-hand lane 406 behind the large truck 404,in a similar manner as it calculated the risk cost associated itsexpected state as the result of active-sensing action 416 (for clarity,these risk costs may be referred to as the current risk cost andexpected risk cost, respectively). Vehicle 402 may then subtract theexpected risk cost from the current risk cost to determine the risk costfor the active-sensing action 416.

Note that when calculating the current risk cost, vehicle 402 may takeinto account some or all of the same bad events as it considers whencalculating the expected risk cost. However, because different badevents are possible in the current and expected states, the current riskcost might be based on partially or entirely different bad events thanthe expected risk cost is based upon.

Once it has determined an information-improvement value and a risk costfor active-sensing action 416, vehicle 402 may determine whetheractive-sensing action 416 is advisable. For example, consider theabove-described scenario where the information-improvement expectationfor active-sensing action 416 is determined to be 30, and theabove-described scenario where no right turn is planned at traffic light412 and the risk cost is determined to be 8. In this case, vehicle 402may determine the score to be equal to 22 by subtracting the risk costfrom the information-improvement expectation. If the threshold fordeeming an active-sensing action to be advisable is set at a score of10, for example, then vehicle 402 may determine that active-sensingaction 416 is advisable and perform active-sensing action 16 in aneffort to improve its image data for traffic light 412.

Now consider the same scenario, except that now, a right turn is plannedat traffic light 412. In this case, vehicle 412 may determine the riskcost for active-sensing action 416 to be 58. Thus, if the thresholdscore is set at 10, vehicle 412 may refrain from performingactive-sensing action 416.

It should be understood that the specific implementation detailsdescribed in reference to scenario 400 are provided for illustrativepurposes, and should not be considered limiting. Other implementationsof example methods and systems are possible, without departing from thescope of the invention.

In some embodiments, an autonomous vehicle (or systems associated withthe autonomous vehicle) may weigh expected information improvement andrisk cost involved with varying degrees or extents of an active sensingaction to determine the degree or extent of an active-sensing actionthat is appropriate (if any). As an example, risk costs and informationimprovement expectations could be used to determine the amount and/ordirection of motion that results in an amount of improvement that isacceptable given the corresponding risk cost. For instance, a firstmovement in a given direction may have an undesirable amount of risk,but a second movement, which is smaller and/or in a slightly differentdirection, may still provide some information improvement, while stayingwithin an acceptable risk threshold for the amount of informationgained. In such case, the autonomous vehicle may initiate the secondmovement, even if the first movement is not advisable given theassociated risk cost and information improvement expectation.

FIG. 6 is a flow chart illustrating a method 600, according to anexample embodiment. Method 600 provides an iterative approach fordetermining the appropriate degree or extent (if any) of anactive-sensing action. In particular, before an active-sensing action isinitiated, method 600 may be implemented to adjust the degree of theactive-sensing action iteratively until the risk is below a threshold.Further, on some or all iterations of method 600, the risk threshold maybe adjusted to correspond to the information-improvement expectation ofthe adjusted active-sensing action.

More specifically, method 600 may involve a computing system receivinginformation from one or more sensors of an autonomous vehicle, as shownby block 602. In an example embodiment, one or more control processesfor the autonomous vehicle are based upon the information received atblock 602. The computing system may then determine aninformation-improvement expectation that corresponds to anactive-sensing action, as shown by block 604. The computing system mayalso determine a risk cost that corresponds to the active-sensingaction, as shown by block 606.

The computing system may then determine whether or not the risk cost isless than a threshold risk cost, as shown by block 608. In an exampleembodiment, the threshold risk cost corresponds to theinformation-improvement expectation. That is, the threshold risk costmay be set to a higher value if the information-improvement expectationdetermined at block 604 is higher, and set to a lower value if theinformation-improvement expectation determined at block 604 is lower.

Continuing with method 600, if the risk cost is less than the thresholdrisk cost, then the computing system may initiate the active-sensingaction, as shown by block 610. On the other hand, if the risk cost isgreater than or equal than the threshold risk cost, then the computingsystem makes an adjustment to the active-sensing action that is expectedto affect the determined risk cost, as shown by block 612. In an exampleembodiment, the adjustment to the active-sensing action may be made withthe expectation of lowering the associated risk cost.

Various types of adjustments to the active sensing action are possibleat block 612. In general, the adjustment may involve changing the extentor degree of the active-sensing action. For example, making theadjustment may involve one or more of: (a) adjusting an amount of achange in speed of the autonomous vehicle, (b) adjusting at least one ofa distance and a direction of a change in position of the autonomousvehicle relative to an aspect of an environment of the autonomousvehicle, (c) adjusting at least one of a distance and a direction of achange in position of the autonomous vehicle within a lane, (d)adjusting at least one of a distance and a direction of a change inposition of at least one of the sensors, and (e) adjusting a degree of achange in operation of at least one of the sensors. Other examples ofadjustments to active-sensing actions are also possible.

In some cases, the adjustment to the active-sensing action may alsoaffect (e.g., increase or decrease) the value correspondinginformation-improvement expectation. In such cases, method 600 mayfurther involve re-determining the threshold risk cost based on thechanged value of the information-improvement expectation. In any case,if the risk cost is greater than or equal than the threshold risk cost,then method 600 involves repeating blocks 604 to 608 for the adjustedactive-sensing action until either: (a) the determined risk cost isdetermined to be less than the threshold risk cost at block 608, or (b)a determination is made that no further adjustments should be made tothe active-sensing action, as shown by block 614.

In the event that a determination is made that no further adjustmentsshould be made to the active-sensing action, then method 600 may endwithout initiating the active-sensing action, as shown by block 616.Note that block 614 may involve a background process that checks whetherto continue trying to determine an appropriate degree for theactive-sensing action. In such case, the arrangement of block 614 maynot reflect the timing of the background process with respect to theother parts of method 600. More specifically, since block 614 may be abackground process that is carried out in parallel with method 600, itis possible that a determination may be made that no active-sensingaction is appropriate at any point in time while method 600 is beingcarried out.

For example, such a background process may check whether the conditionsthat indicated a need for the active-sensing action have changed suchthat it is no longer necessary and/or beneficial (e.g., if anactive-sensing action was intended to provide more information about astoplight, the active-sensing action may no longer be needed, to anydegree, if the autonomous vehicle has since driven past the stoplight).As another example, a timer could limit the amount of time fordetermining whether any degree of an active sensing action isappropriate. Other examples are also possible.

In other embodiments, block 614 may involve a periodic check as towhether the active-sensing action would still be beneficial. In suchembodiments, block 614 may be carried out during each iteration ofmethod 600, or may be carried out intermittently (e.g., every fourthiteration). Other variations on method 600 are also possible.

FIG. 7 is a flow chart illustrating another method 700, according to anexample embodiment. Method 700 provides a single-pass approach fordetermining the appropriate degree or extent (if any) of anactive-sensing action. In particular, method 700 may use a risk-costframework and information-improvement expectation framework over a rangeof degrees to which the active-sensing action can be performed, andidentify a particular degree or extent to perform the action, whichbalances risk cost with expected information improvement.

More specifically, method 700 involves a computing system receivinginformation from one or more sensors of an autonomous vehicle, as shownby block 702. In an example embodiment, one or more control processesfor the autonomous vehicle are based upon the information received atblock 702. Further, the computing system determines a risk-costframework, which indicates risk costs across a range of degrees to whichan active-sensing action can be performed, as shown by block 704. (Asdescribed in reference to other embodiments, the active-sensing actionis an action that is performable by the autonomous vehicle topotentially improve the information upon which at least one of thecontrol processes for the autonomous vehicle is based.) The computingsystem also determines an information-improvement expectation frameworkthat indicates information-improvement expectations across the range ofdegrees to which the active-sensing action can be performed, as shown byblock 706.

At block 704, the risk-cost framework may be determined in various ways.As noted above, the risk-cost framework indicates risk costs across arange of degrees to which an active-sensing action can be performed. Forinstance, determining the risk-cost framework may involve determiningrisk cost data across one or more of: (a) a range of speed changes forthe autonomous vehicle, (b) a range of positional changes (e.g., over arange of distance, a range of directions, and/or a range oforientations) of the autonomous vehicle relative to an aspect of anenvironment of the autonomous vehicle, (c) a range of positional changesof the autonomous vehicle within a lane, (d) a range of positionalchanges of at least one of the sensors, and (e) a range of operationaladjustments to at least one of the sensors.

As a specific example of a risk-cost framework, consider a scenariowhere the potential active sensing action is a movement of theautonomous vehicle the left on the road, in an effort to improve sensordata that is being blocked by another vehicle directly in front of theautonomous vehicle. In this scenario, a risk-cost framework may bedetermined over a range of distances that the autonomous vehicle mightmove to the left. For instance, risk costs may be determined at each ofa sampling of distances between one millimeter and two meters. Ofcourse, other ranges are possible depending upon the particularimplementation and/or the particular scenario. Alternatively, therisk-cost framework may be determined to be a formula, which takesdistance as an input, and outputs the risk cost associated with amovement of the distance to the left by the autonomous vehicle. Othertypes of risk-cost frameworks are also possible.

As an additional example of a risk-cost framework, consider a scenariowhere the potential active sensing action is a rotation of one of theautonomous vehicle's sensors, in an effort to improve the sensor datathat is being obtained by the sensor. In this scenario, a risk-costframework may be determined over a range of degrees through which thesensor might be rotated. For instance, risk costs may be determined ateach of a sampling of degrees of rotation from an initial position;e.g., by determining a risk cost at every five-degree interval betweenfive and ninety degrees. (Here again, other ranges are possibledepending upon the particular implementation and/or the particularscenario.) Alternatively, the risk-cost framework may be determined tobe a formula, which takes the degrees of rotation as an input, andoutputs the risk cost associated with such rotation by the sensor.

At block 706, the information-improvement expectation framework may bedetermined in various ways. As noted above, information-improvementexpectation framework indicates information-improvement expectationsacross the range of degrees to which the active-sensing action can beperformed. For instance, determining the information-improvementexpectation framework may involve determining information-improvementexpectation data across one or more of: (a) a range of speed changes forthe autonomous vehicle, (b) a range of positional changes (e.g., over arange of distance, a range of directions, and/or a range oforientations) of the autonomous vehicle relative to an aspect of anenvironment of the autonomous vehicle, (c) a range of positional changesof the autonomous vehicle within a lane, (d) a range of positionalchanges of at least one of the sensors, and (e) a range of operationaladjustments to at least one of the sensors.

As a specific example of an information-improvement expectationframework, consider again the scenario where the potential activesensing action is a movement of the autonomous vehicle the left on theroad, in an effort to improve sensor data that is being blocked byanother vehicle directly in front of the autonomous vehicle. In thisscenario, the information-improvement expectation framework may bedetermined over a range of distances that the autonomous vehicle mightmove to the left. For instance, information-improvement expectations maybe determined at each of a sampling of distances between one millimeterand two meters. Of course, other ranges are possible depending upon theparticular implementation and/or the particular scenario. Alternatively,the information-improvement expectation framework may be determined tobe a formula, which takes distance as an input, and outputs theinformation-improvement expectation associated with a movement of theinput distance to the left by the autonomous vehicle.

As an additional example of an information-improvement expectationframework, consider again the scenario where the potential activesensing action is a rotation of one of the autonomous vehicle's sensors,in an effort to improve the sensor data that is being obtained by thesensor. In this scenario, the information-improvement expectationframework may be determined over a range of degrees through which thesensor might be rotated. For instance, an information-improvementexpectation may be determined at each of a sampling of degrees ofrotation from an initial position; e.g., by determining theinformation-improvement expectation at every five-degree intervalbetween five and ninety degrees. (Here again, other ranges are possibledepending upon the particular implementation and/or the particularscenario.) Alternatively, the information-improvement expectationframework may be determined to be a formula, which takes the degrees ofrotation as an input, and outputs the information-improvementexpectation associated with such rotation by the sensor. Other types ofinformation-improvement expectation frameworks are also possible.

Referring again to method 700, the computing system may apply therisk-cost framework and the determined information-improvementexpectation framework to determine a degree (if any) to which theactive-sensing action should be performed, as shown by block 708. Thecomputing system then initiates the active-sensing action of thedetermined degree, as shown by block 710.

In an example embodiment, the degree of the active-sensing actiondetermined at block 708 may have a score that is less than a thresholdscore, and/or that is greater than the score associated with otherpossible degrees of the active-sensing action. The score for a givendegree of an active-sensing action may be determined based on the riskcost and information-improvement expectation corresponding to the givendegree, as described herein in reference to other embodiments.

In some embodiments, the disclosed methods may be implemented bycomputer program instructions encoded on a non-transitorycomputer-readable storage media in a machine-readable format, or onother non-transitory media or articles of manufacture. FIG. 5 is aschematic illustrating a conceptual partial view of an example computerprogram product that includes a computer program for executing acomputer process on a computing device, arranged according to at leastsome embodiments presented herein.

In some embodiments, the example computer program product 500 isprovided using a signal bearing medium 502. The signal bearing medium502 may include one or more programming instructions 504 that, whenexecuted by one or more processors may provide functionality or portionsof the functionality described above with respect to FIGS. 1-4. In someexamples, the signal bearing medium 502 may encompass acomputer-readable medium 506, such as, but not limited to, a hard diskdrive, a Compact Disc (CD), a Digital Video Disk (DVD), a digital tape,memory, etc. In some implementations, the signal bearing medium 502 mayencompass a computer recordable medium 508, such as, but not limited to,memory, read/write (R/W) CDs, R/W DVDs, etc. In some implementations,the signal bearing medium 502 may encompass a communications medium 510,such as, but not limited to, a digital and/or an analog communicationmedium (e.g., a fiber optic cable, a waveguide, a wired communicationslink, a wireless communication link, etc.). Thus, for example, thesignal bearing medium 502 may be conveyed by a wireless form of thecommunications medium 510.

The one or more programming instructions 504 may be, for example,computer executable and/or logic implemented instructions. In someexamples, a computing device such as the computer system 112 of FIG. 1may be configured to provide various operations, functions, or actionsin response to the programming instructions 504 conveyed to the computersystem 112 by one or more of the computer readable medium 506, thecomputer recordable medium 508, and/or the communications medium 510.

The non-transitory computer readable medium could also be distributedamong multiple data storage elements, which could be remotely locatedfrom each other. The computing device that executes some or all of thestored instructions could be a vehicle, such as the vehicle 200illustrated in FIG. 2. Alternatively, the computing device that executessome or all of the stored instructions could be another computingdevice, such as a server.

While various aspects and embodiments have been disclosed herein, otheraspects and embodiments will be apparent to those skilled in the art.The various aspects and embodiments disclosed herein are for purposes ofillustration and are not intended to be limiting, with the true scopeand spirit being indicated by the following claims.

We claim:
 1. A computer-implemented method comprising: (i) receiving, bya computing system, information from one or more sensors of anautonomous vehicle, wherein one or more control processes for theautonomous vehicle are based upon the information; (ii) determining, bythe computing system, an information-improvement expectation thatcorresponds to an active-sensing action, wherein the active-sensingaction comprises an action that is performable by the autonomous vehicleto potentially improve the information upon which at least one of thecontrol processes for the autonomous vehicle is based; (iii)determining, by the computing system, a risk cost that corresponds tothe active-sensing action; (iv) determining, by the computing system,whether or not the risk cost is less than a threshold risk cost, whereinthe threshold risk cost corresponds to the information-improvementexpectation; (v) if the risk cost is less than the threshold risk cost,then initiating the active-sensing action; and (vi) otherwise, if therisk cost is greater than or equal to the threshold risk cost, thenmaking an adjustment to the active-sensing action.
 2. The method ofclaim 1, wherein the one or more sensors comprise one or more of: (a) atleast one camera, (b) at least one microphone, (c) a Global PositioningSystem (GPS), (d) at least one accelerometer, (e) at least onegyroscope, (f) at least one compass, (g) a RADAR, (h) at least one laserrangefinder, (i) LIDAR, (j) at least one steering sensor, (k) a throttlesensor, and (l) at least one brake sensor.
 3. The method of claim 1,wherein the active-sensing action comprises one or more of: (a) a changein speed of the autonomous vehicle, (b) a change in position of theautonomous vehicle relative to an aspect of an environment of theautonomous vehicle, (c) a change in position of the autonomous vehiclewithin a lane, (d) a change in position of at least one of the sensors,(e) a change in operation of at least one of the sensors, and/or (f) achange to the processing of sensor data from at least one of thesensors, wherein the sensor data provides information upon which atleast one control process is based.
 4. The method of claim 1, whereindetermining the information-improvement expectation that corresponds tothe active-sensing action comprises: determining an information value ofsensor data in a first state of the autonomous vehicle; determining anexpected information value of the sensor data that is expected to beprovided in a second state of the autonomous vehicle; and subtractingthe information value from the expected information value to determinean information-improvement value for the active sensing action.
 5. Themethod of claim 1, wherein determining the information-improvementexpectation that corresponds to the active-sensing action comprises: (i)determining an information-improvement value of a given improvement tothe information upon which the at least one of the control process isbased; (ii) determining a probability of the given improvement occurringas a result of the autonomous vehicle performing the active-sensingaction; and (iii) multiplying the information value of the givenimprovement by the probability of the given improvement occurring todetermine an expected information value for the given improvement. 6.The method of claim 1, wherein the risk cost is indicative of a riskassociated with the autonomous vehicle performing the active-sensingaction.
 7. The method of claim 1, wherein the risk cost is indicative ofa change in risk associated with the autonomous vehicle performing theactive-sensing action.
 8. The method of claim 1, wherein determining therisk cost that corresponds to the active-sensing action comprisesdetermining a risk penalty for one or more bad events that could occuras a result of the active-sensing action.
 9. The method of claim 8,wherein determining the risk penalty for a given bad event comprises:determining a risk magnitude for the given bad event; determining theprobability of the given bad event as a result of the active-sensingaction; and multiplying the risk magnitude for the given bad event bythe probability of the given bad event occurring to determine the riskpenalty for the given bad event.
 10. The method of claim 1: wherein theactive-sensing action comprises a movement of the autonomous vehicleinto a different position relative to at least one other vehicle topotentially improve environmental information provided by image datafrom a camera; and wherein determining the information-improvementexpectation comprises determining an expected improvement to a processthat is based on the environmental information, wherein the expectedimprovement corresponds to the movement of the autonomous vehicle intothe different position relative to the at least one other vehicle. 11.The method of claim 1: wherein the active-sensing action comprises anaction by the autonomous vehicle to potentially improve a view of a GPSsatellite; and wherein determining the information-improvementexpectation that corresponds to the active-sensing action comprisesdetermining an expected improvement to a process that is based on datafrom a GPS signal, wherein the expected improvement corresponds to theaction by the autonomous vehicle to improve the view of the GPSsatellite.
 12. The method of claim 1, wherein making the adjustment tothe active-sensing action comprises changing the extent of theactive-sensing action.
 13. The method of claim 12, wherein changing theextent of the active-sensing action comprises one or more of: (a)adjusting an amount of a change in speed of the autonomous vehicle, (b)adjusting at least one of a distance and a direction of a change inposition of the autonomous vehicle relative to an aspect of anenvironment of the autonomous vehicle, (c) adjusting at least one of adistance and a direction of a change in position of the autonomousvehicle within a lane, (d) adjusting at least one of a distance and adirection of a change in position of at least one of the sensors, and(e) adjusting a degree of a change in operation of at least one of thesensors.
 14. The method of claim 1, wherein the adjustment to theactive-sensing action changes at least one of theinformation-improvement expectation and the determined risk costcorresponding to the active-sensing action.
 15. The method of claim 1,further comprising, if the risk cost is greater than or equal to thethreshold risk cost, repeating (ii) to (vi) for the adjustedactive-sensing action until either the determined risk cost is less thanthe threshold risk cost, or a determination is made that no furtheradjustments should be made to the active-sensing action.
 16. Anautonomous-vehicle system comprising: one or more sensor interfacesoperable to receive data from one or more sensors of an autonomousvehicle; and a computer system configured to: (i) receive, via the oneor more sensor interfaces, information from the one or more sensors,wherein one or more control processes for the autonomous vehicle arebased upon the information; (ii) determine an information-improvementexpectation that corresponds to an active-sensing action, wherein theactive-sensing action comprises an action that is performable by theautonomous vehicle to potentially improve the information upon which atleast one of the control processes for the autonomous vehicle is based;(iii) determine a risk cost that corresponds to the active-sensingaction; (iv) determine whether or not the risk cost is less than athreshold risk cost, wherein the threshold risk cost corresponds to theinformation-improvement expectation; (v) if the risk cost is less thanthe threshold risk cost, then initiate the active-sensing action; and(vi) if the risk cost is greater than or equal than the threshold riskcost, then make an adjustment to the active-sensing action.
 17. Theautonomous-vehicle system of claim 16, wherein the adjustment to theactive-sensing action comprises a change in extent of the active-sensingaction.
 18. The autonomous-vehicle system of claim 16, wherein thechange in extent the active-sensing action comprises one or more of: (a)an adjustment to an amount of a change in speed of the autonomousvehicle, (b) an adjustment to at least one of a distance and a directionof a change in position of the autonomous vehicle relative to an aspectof an environment of the autonomous vehicle, (c) an adjustment to atleast one of a distance and a direction of a change in position of theautonomous vehicle within a lane, (d) an adjustment to at least one of adistance and a direction of a change in position of at least one of thesensors, and (e) an adjustment to a degree of a change in operation ofat least one of the sensors.
 19. A method comprising: receiving, by acomputing system, information from one or more sensors of an autonomousvehicle, wherein one or more control processes for the autonomousvehicle are based upon the information; determining, by the computingsystem, a risk-cost framework that indicates risk costs across a rangeof degrees to which an active-sensing action can be performed, whereinthe active-sensing action comprises an action that is performable by theautonomous vehicle to potentially improve the information upon which atleast one of the control processes for the autonomous vehicle is based;determining, by the computing system, an information-improvementexpectation framework that indicates information-improvementexpectations across the range of degrees to which the active-sensingaction can be performed; applying, by the computing system, therisk-cost framework and the information-improvement expectationframework to determine a degree to which the active-sensing actionshould be performed; and initiating the active-sensing action of thedetermined degree.
 20. The method of claim 19, wherein the determineddegree maximizes the information-improvement expectation correspondingto the active-sensing action, with a constraint that a scorecorresponding to the active-sensing action is less than a thresholdscore.
 21. The method of claim 19, wherein determining theinformation-improvement expectation framework comprises: determininginformation-improvement expectation data across one or more of: (a) arange of speed changes for the autonomous vehicle, (b) a range ofpositional changes of the autonomous vehicle relative to an aspect of anenvironment of the autonomous vehicle, (c) a range of positional changesof the autonomous vehicle within a lane, (d) a range of positionalchanges of at least one of the sensors, and (e) a range of operationaladjustments to at least one of the sensors.